Localization-Based Sensor Validation Using The Kullback-Leibler Divergence

A sensor validation criteria based on the sensor's object localization accuracy is proposed. Assuming that the true probability distribution of an object or event in space f(x) is known and a spatial likelihood function (SLF) #(x) for the same object or event in space is obtained from a sensor, then the expected value of the SLF E[#(x)] is proposed as a suitable validity metric for the sensor, where the expectation is performed over the distribution f(x). It is shown that for the class of increasing linear log-likelihood SLFs, the proposed validity metric is equivalent to the Kullback-Leibler distance between f(x) and the unknown sensor-based distribution g(x) where the SLF #(x) is an observable increasing function of the unobservable g(x). The proposed technique is illustrated through several simulated and experimental examples